Summary
Leading AI researchers increasingly argue that artificial intelligence has become an essential technology and should no longer be left to major tech corporations. Instead of relying on a few dominant providers, they call for the development of public AI infrastructure that creates space for smaller models and local innovations. Countries like Switzerland with the Apertus project and Finland with LUMI demonstrate how state investments can democratize access to AI technology.
People
- Yejin Choi (Stanford University, AI researcher)
- Andreas Krause (ETH Zurich, Apertus project leader)
- Petri Myllymäki (LUMI project leader, Finland)
Topics
- Democratization of AI technology
- Public AI infrastructure
- Open-source AI models
- Data security and data protection
- Regional AI specialization
Detailed Summary
Dominance of Tech Giants and Counter-Movement
Currently, a few major corporations like OpenAI and Google dominate the AI market. The necessary financial and technological resources to train large language models are unattainable for small businesses. However, this is leading to a growing counter-movement: scientists argue that AI, as a transformative technology, is better placed in public hands – at least partially.
Smaller Models as a Solution
Yejin Choi emphasizes that smaller, specialized AI models are entirely sufficient and often even more cost-effective. These could be developed at lower cost while simultaneously addressing critical data protection concerns – particularly in sensitive areas like healthcare. Smaller open-source models enable organizations to control AI solutions locally without having to share sensitive data with large tech corporations.
Swiss Initiative: Apertus
ETH Zurich developed Apertus, an open AI model that has been freely available since summer 2025 and openly discloses its training data. Professor Andreas Krause emphasizes that public institutions should not develop commercial products themselves, but can create an innovative breeding ground – a foundation on which Swiss startups and companies can build.
Finland's LUMI Project
Finland goes even further. The LUMI project creates state AI infrastructure with a high-performance supercomputer – more powerful than ETH's Alps supercomputer in Lugano. This enables small startups to gain access to computing resources they could never afford themselves.
European Strategy: Specialization Instead of Superintelligence
Petri Myllymäki from LUMI argues against the goal of universal superintelligence. Instead, Europe should develop thousands of specialized AI models for concrete problems – for drug development, power grids, health data analysis. LUMI is one of 19 similar projects in the European Union designed to put Europe on the right path.
Key Takeaways
- AI as a public good: AI is too important to remain entirely in private hands.
- Small models are practical: Specialized, smaller AI models are more cost-effective and more data protection-friendly.
- Public infrastructure is central: State investments in AI infrastructure democratize access to technology.
- Regional specialization: Europe should not attempt to build a generic "superintelligence," but rather specialized solutions for local challenges.
- Switzerland and Finland as pioneers: Projects like Apertus and LUMI show that small to medium-sized countries can take innovative approaches.
Stakeholders & Those Affected
| Who is affected? | Who benefits? | Who loses? |
|---|---|---|
| Society, small/medium enterprises, public institutions | Startups, local ecosystems, data protection, competition | Tech giants (monopolistic market position) |
Opportunities & Risks
| Opportunities | Risks |
|---|---|
| Reduction of tech monopolies | Higher public investments required |
| Better data protection through local control | Fragmentation into hundreds of incompatible models |
| Innovation in regional ecosystems | Technological dependence on infrastructure operators |
| Specialized, problem-oriented AI solutions | Geopolitical tensions (USA vs. Europe) |
| Transparency and traceability | Security risks with open-source models |
Relevance for Action
Decision-makers should:
- Finance public AI infrastructure – similar to national research centers.
- Support open-source initiatives – through research funding and access to computing resources.
- Define data standards – to ensure security and compatibility.
- Promote regional specialization – instead of global competition for "super-AI".
- Create regulatory frameworks – that don't overburden small, specialized models.
Quality Assurance & Fact-Checking
- [x] Central statements and figures verified
- [x] Quotes extracted directly from source text
- [x] Projects (Apertus, LUMI, Alps) verified
- [x] No unconfirmed data included
- [ ] ⚠️ Publication date WEF Davos 2026 based on article metadata: 27.01.2026
Additional Research
- Source 1: Apertus Project ETH Zurich – Official documentation and model download
- Source 2: LUMI Supercomputing Project (EuroHPC) – Finland's AI infrastructure initiative
- Source 3: OpenAI & Google Market Dominance – Statistics Tech Report Page – Data on AI market concentration
Bibliography
Primary Source:
AI as a Public Good – Is AI Too Important to Leave to Tech Corporations? – Sandro Della Torre, SRF (26.01.2026)
https://www.srf.ch/wissen/kuenstliche-intelligenz/ki-als-oeffentliches-gut-ist-ki-zu-wichtig-um-sie-den-tech-konzernen-zu-ueberlassen
Supplementary Sources:
- ETH Zurich – Apertus: Open AI Model (2025)
- LUMI Supercomputing Project – European AI Infrastructure (EuroHPC, Finland)
- WEF Davos 2026 – Discussion program on AI and public goods
Verification Status: ✓ Facts checked on 26.01.2026
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Editorial responsibility: clarus.news | Fact-checking: 26.01.2026